Background Diagnostic models play an important role in the management of suspected angina but provide no explicit information about prognosis. The objective of this study was to develop a prognostic model to predict 10-year coronary mortality in patients presenting for the first time with suspected angina to complement the updated Diamond-Forrester diagnostic model of disease probability.1
Methods A multicentre cohort of 8762 patients with suspected angina was followed up for a median of 10 years during which 233 coronary deaths were observed. Developmental (n = 4412) and validation (n = 4350) prognostic models based on clinical data available at first presentation showed good performance with close agreement and the final model utilised all 8762 patients to maximize power.
Results The prognostic model showed strong associations with coronary mortality forage, sex, typicality of chest pain, smoking status, diabetes, pulse rate and ECG findings. Model discrimination was good (C statistic 0.83), patients in the highest risk quarter accounting for 173 coronary deaths during follow-up (10 year risk of death: 8.7%) compared with a total of 60 deaths in the three lower risk quarters. Observed 10-year coronary mortality increased with increasing estimates of disease probability, ranging from 0.2% to 25.4% with CAD probability of <10% and >90%, respectively. However, when our prognostic model was simplified to incorporate only those factors used by the updated Diamond-Forrester (age, sex and character of symptoms) it under-estimated coronary mortality risk, particularly in patients with risk factors.
Conclusion For the first time in patients with suspected angina, a prognostic model is presented based on simple clinical factors available at the initial cardiological assessment. The model discriminated powerfully between patients at high risk and at lower risk of coronary death during the 10-year follow-up period. Its potential clinical utility was reflected in the prognostic value it added to the updated Diamond-Forrester diagnostic model of disease probability.
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